scholarly journals Spatial variability and dynamics of soil pH, soil organic carbon and matter content: The case of the Wonji Shoa sugarcane plantation

2019 ◽  
Vol 42 (1) ◽  
pp. 59-66
Author(s):  
Megersa Olumana Dinka ◽  
Meseret Dawit
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Soil Ph ◽  
Ph Value ◽  
Bottom Layer ◽  
Matter Content ◽  
Soil Layers ◽  
Emission Effect ◽  
Greenhouse Emission

Abstract This study presents the spatial variability and dynamics of soil organic carbon (SOC), soil organic matter (SOM) and soil pH contents at the Wonji Shoa Sugar Estate (WSSE), Ethiopia. Soil samples were collected immediately after the sugarcane was harvested and then analysed for SOC, SOM and pH content using standard procedures. The analysis results showed that the pH value varied between 6.7–8.4 (neutral to moderately alkaline) and 7.3–8.5 (neutral to strongly alkaline) for the top and bottom soil profiles, respectively. The SOM content is in the range of 1.1–6.7% and 0.74–3.3% for the upper and lower soil layers, respectively. Nearly 45% of the samples demonstrated a SOM content below the desirable threshold (<2.1%) in the bottom layer and, hence, inadequate. Moreover, most of the topsoil layer (95%) has an SOM content exceeding the desirable limit and hence is categorized within the normal range. Interestingly, the SOC content showed a spatial variability in both the surface and sub-surface soil layers. A lower SOC and SOM content was found for the sub-soil in the south and southwestern part of the plantation. A further decline in the SOC and SOM content may face the estate if the current waterlogging condition continues in the future for a long period. Overall, the study result emphasizes the need to minimize the pre-harvest burning of sugarcane and action is needed to change the irrigation method to green harvesting to facilitate the SOC retention in the soil and minimize the greenhouse emission effect on the environment, hence improving soil quality in the long-term.

Spatial variability of soil organic carbon in a typical watershed in the source area of the middle Dan River, China

Soil Research ◽  
2013 ◽  
Vol 51 (1) ◽  
pp. 41 ◽  
Author(s):  
Guo-Ce Xu ◽  
Zhan-Bin Li ◽  
Peng Li ◽  
Ke-Xin Lu ◽  
Yun Wang
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Spatial Heterogeneity ◽  
Spatial Variation ◽  
Soil Depth ◽  
Source Area ◽  
Soil Layers ◽  
Land Use Types

Soil organic carbon (SOC) plays an important role in maintaining and improving soil fertility and quality, in addition to mitigating climate change. Understanding SOC spatial variability is fundamental for describing soil resources and predicting SOC. In this study, SOC content and SOC mass were estimated based on a soil survey of a small watershed in the Dan River, China. The spatial heterogeneity of SOC distribution and the impacts of land-use types, elevation, slope, and aspect on SOC were also assessed. Field sampling was carried out based on a 100 m by 100 m grid system overlaid on the topographic map of the study area, and samples were collected in three soil layers to a depth of 40 cm. In total, 222 sites were sampled and 629 soil samples were collected. The results showed that classical kriging could successfully interpolate SOC content in the watershed. Contents of SOC showed strong spatial heterogeneity based on the values of the coefficient of variation and the nugget ratio, and this was attributed largely to the type of land use. The range of the semi-variograms increased with increasing soil depth. The SOC content in the soil profile decreased as soil depth increased, and there were significant (P < 0.01) differences among the three soil layers. Land use had a great impact on the SOC content. ANOVA indicated that the spatial variation of SOC contents under different land use types was significant (P < 0.05). The SOC mass of different land-use types followed the order grassland > forestland > cropland. Mean SOC masses of grassland, forestland, and cropland at a depth of 0–40 cm were 5.87, 5.61, and 5.07 kg m–2, respectively. The spatial variation of SOC masses under different land-use types was significant (P < 0.05). ANOVA also showed significant (P < 0.05) impact of aspect on SOC mass in soil at 0–40 cm. Soil bulk density played an important role in the assessment of SOC mass. In conclusion, carbon in soils in the source area of the middle Dan River would increase with conversion from agricultural land to forest or grassland.

scholarly journals Storage of Soil Organic Carbon and Its Spatial Variability in an Agro-Pastoral Ecotone of Northern China

2020 ◽  
Vol 12 (6) ◽  
pp. 2259
Author(s):  
Yanjiang Zhang ◽  
Qing Zhen ◽  
Pengfei Li ◽  
Yongxing Cui ◽  
Junwei Xin ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Strong Dependence ◽  
Soil Layer ◽  
Northern China ◽  
Structural Factors ◽  
Deep Soil ◽  
Soil Layers

Spatial distribution of soil organic carbon (SOC) is important for the development of ecosystem carbon cycle models and assessment of soil quality. In this study, a total of 732 soil samples from 122 soil profiles (0–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm) were collected by a combination of fixed-point sampling and route surveys in an agro-pastoral ecotone of northern China and the spatial variation of the SOC in the samples was analyzed through classical statistical and geostatistical approaches. The results showed that the SOC contents decreased from 4.31 g/kg in the 0–10 cm to 1.57 g/kg in the 80–100 cm soil layer. The spatial heterogeneity of the SOC exhibited moderate and strong dependence for all the soil layers owing to random and structural factors including soil texture, topography, and human activities. The spatial distributions of the SOC increased gradually from northeast to southwest in the 0–40 cm soil layers, but there was no general trend in deep soil layers and different interpolation methods resulted in the inconsistent spatial distribution of SOC. The storage of SOC was expected to be 25 Tg in the 0–100 cm soil depths for the whole area of 7692 km2. The SOC stocks estimated by two interpolation approaches were very close (25.65 vs. 25.86 Tg), but the inverse distance weighting (IDW) interpolation generated a more detailed map of SOC and with higher determination coefficient (R2); therefore, the IDW was recognized as an appropriate method to investigate the spatial variability of SOC in this region.

scholarly journals A Two-Stage Approach to the Estimation of High-Resolution Soil Organic Carbon Storage with Good Extension Capability

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 517
Author(s):  
Sunwei Wei ◽  
Zhengyong Zhao ◽  
Qi Yang ◽  
Xiaogang Ding
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Storage ◽  
Guangdong Province ◽  
Soil Samples ◽  
Estimation Accuracy ◽  
Ann Model ◽  
Soil Layers ◽  
Second Stage ◽  
Organic Carbon Storage

Soil organic carbon storage (SOCS) estimation is a crucial branch of the atmospheric–vegetation–soil carbon cycle study under the background of global climate change. SOCS research has increased worldwide. The objective of this study is to develop a two-stage approach with good extension capability to estimate SOCS. In the first stage, an artificial neural network (ANN) model is adopted to estimate SOCS based on 255 soil samples with five soil layers (20 cm increments to 100 cm) in Luoding, Guangdong Province, China. This method is compared with three common methods: The soil type method (STM), ordinary kriging (OK), and radial basis function (RBF) interpolation. In the second stage, a linear model is introduced to capture the regional differences and further improve the estimation accuracy of the Luoding-based ANN model when extending it to Xinxing, Guangdong Province. This is done after assessing the generalizability of the above four methods with 120 soil samples from Xinxing. The results for the first stage show that the ANN model has much better estimation accuracy than STM, OK, and RBF, with the average root mean square error (RMSE) of the five soil layers decreasing by 0.62–0.90 kg·m−2, R2 increasing from 0.54 to 0.65, and the mean absolute error decreasing from 0.32 to 0.42. Moreover, the spatial distribution maps produced by the ANN model are more accurate than those of other methods for describing the overall and local SOCS in detail. The results of the second stage indicate that STM, OK, and RBF have poor generalizability (R2 < 0.1), and the R2 value obtained with ANN method is also 43–56% lower for the five soil layers compared with the estimation accuracy achieved in Luoding. However, the R2 of the linear models built with the 20% soil samples from Xinxing are 0.23–0.29 higher for the five soil layers. Thus, the ANN model is an effective method for accurately estimating SOCS on a regional scale with a small number of field samples. The linear model could easily extend the ANN model to outside areas where the ANN model was originally developed with a better level of accuracy.

Soil organic carbon storage by shaded and unshaded coffee systems and its implications for climate change mitigation in China

2021 ◽  
pp. 1-8
Author(s):  
Ziwei Xiao ◽  
Xuehui Bai ◽  
Mingzhu Zhao ◽  
Kai Luo ◽  
Hua Zhou ◽  
...  
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Multiple Linear Regression ◽  
Carbon Storage ◽  
Soil Ph ◽  
Soil Bulk Density ◽  
Soil N ◽  
And Storage

Abstract Shaded coffee systems can mitigate climate change by fixation of atmospheric carbon dioxide (CO2) in soil. Understanding soil organic carbon (SOC) storage and the factors influencing SOC in coffee plantations are necessary for the development of sound land management practices to prevent land degradation and minimize SOC losses. This study was conducted in the main coffee-growing regions of Yunnan; SOC concentrations and storage of shaded and unshaded coffee systems were assessed in the top 40 cm of soil. Relationships between SOC concentration and factors affecting SOC were analysed using multiple linear regression based on the forward and backward stepwise regression method. Factors analysed were soil bulk density (ρb), soil pH, total nitrogen of soil (N), mean annual temperature (MAT), mean annual moisture (MAM), mean annual precipitation (MAP) and elevations (E). Akaike's information criterion (AIC), coefficient of determination (R2), root mean square error (RMSE) and residual sum of squares (RSS) were used to describe the accuracy of multiple linear regression models. Results showed that mean SOC concentration and storage decreased significantly with depth under unshaded coffee systems. Mean SOC concentration and storage were higher in shaded than unshaded coffee systems at 20–40 cm depth. The correlations between SOC concentration and ρb, pH and N were significant. Evidence from the multiple linear regression model showed that soil bulk density (ρb), soil pH, total nitrogen of soil (N) and climatic variables had the greatest impact on soil carbon storage in the coffee system.

Response of soil organic carbon spatial variability to the expansion of scale in the uplands of Northeast China

Geoderma ◽  
2010 ◽  
Vol 154 (3-4) ◽  
pp. 302-310 ◽  
Author(s):  
D.D. Wang ◽  
X.Z. Shi ◽  
X.X. Lu ◽  
H.J. Wang ◽  
D.S. Yu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Northeast China

Effect of Soil Sampling Density on Detected Spatial Variability of Soil Organic Carbon in a Red Soil Region of China

Pedosphere ◽  
2011 ◽  
Vol 21 (2) ◽  
pp. 207-213 ◽  
Author(s):  
Dong-Sheng YU ◽  
Zhong-Qi ZHANG ◽  
Hao YANG ◽  
Xue-Zheng SHI ◽  
Man-Zhi TAN ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Red Soil ◽  
Soil Sampling ◽  
Sampling Density

scholarly journals Soil greenhouse gas fluxes from different tree species on Taihang Mountain, North China

2014 ◽  
Vol 11 (6) ◽  
pp. 1649-1666 ◽  
Author(s):  
X. P. Liu ◽  
W. J. Zhang ◽  
C. S. Hu ◽  
X. G. Tang
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Temperature ◽  
Soil Ph ◽  
Tree Species ◽  
Soil Bulk Density ◽  
Total N ◽  
Gas Fluxes ◽  
Greenhouse Gas Fluxes

Abstract. The objectives of this study were to investigate seasonal variation of greenhouse gas fluxes from soils on sites dominated by plantation (Robinia pseudoacacia, Punica granatum, and Ziziphus jujube) and natural regenerated forests (Vitex negundo var. heterophylla, Leptodermis oblonga, and Bothriochloa ischcemum), and to identify how tree species, litter exclusion, and soil properties (soil temperature, soil moisture, soil organic carbon, total N, soil bulk density, and soil pH) explained the temporal and spatial variation in soil greenhouse gas fluxes. Fluxes of greenhouse gases were measured using static chamber and gas chromatography techniques. Six static chambers were randomly installed in each tree species. Three chambers were randomly designated to measure the impacts of surface litter exclusion, and the remaining three were used as a control. Field measurements were conducted biweekly from May 2010 to April 2012. Soil CO2 emissions from all tree species were significantly affected by soil temperature, soil moisture, and their interaction. Driven by the seasonality of temperature and precipitation, soil CO2 emissions demonstrated a clear seasonal pattern, with fluxes significantly higher during the rainy season than during the dry season. Soil CH4 and N2O fluxes were not significantly correlated with soil temperature, soil moisture, or their interaction, and no significant seasonal differences were detected. Soil organic carbon and total N were significantly positively correlated with CO2 and N2O fluxes. Soil bulk density was significantly negatively correlated with CO2 and N2O fluxes. Soil pH was not correlated with CO2 and N2O emissions. Soil CH4 fluxes did not display pronounced dependency on soil organic carbon, total N, soil bulk density, and soil pH. Removal of surface litter significantly decreased in CO2 emissions and CH4 uptakes. Soils in six tree species acted as sinks for atmospheric CH4. With the exception of Ziziphus jujube, soils in all tree species acted as sinks for atmospheric N2O. Tree species had a significant effect on CO2 and N2O releases but not on CH4 uptake. The lower net global warming potential in natural regenerated vegetation suggested that natural regenerated vegetation were more desirable plant species in reducing global warming.

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